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import torch.nn as nn
import torch.nn.functional as F
class MLP(nn.Module):
"""
MLP with a variable number of hidden layers and activation functions.
"""
def __init__(
self,
in_dim: int,
hidden_size: int,
dropout: float,
out_dim: int,
num_layers: int,
activation: str,
):
super(MLP, self).__init__()
self.layers = nn.ModuleList()
# Input layer
self.layers.append(nn.Linear(in_dim, hidden_size))
if dropout != 0:
self.layers.append(nn.Dropout(dropout))
# Hidden layers
for _ in range(num_layers - 1):
self.layers.append(nn.Linear(hidden_size, hidden_size))
if dropout != 0:
self.layers.append(nn.Dropout(dropout))
# Output layer
self.layers.append(nn.Linear(hidden_size, out_dim))
# Activation function
if activation == "relu":
self.activation = F.relu
elif activation == "gelu":
self.activation = F.gelu
else:
raise ValueError(f"Unsupported activation: {activation}")
def forward(self, x):
for i, layer in enumerate(self.layers):
x = layer(x)
if i < len(self.layers) - 1: # Apply activation to all but last layer
x = self.activation(x)
return x
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